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  2. Rank–nullity theorem - Wikipedia

    en.wikipedia.org/wiki/Ranknullity_theorem

    Ranknullity theorem. The ranknullity theorem is a theorem in linear algebra, which asserts: the number of columns of a matrix M is the sum of the rank of M and the nullity of M; and; the dimension of the domain of a linear transformation f is the sum of the rank of f (the dimension of the image of f) and the nullity of f (the dimension of ...

  3. Jordan normal form - Wikipedia

    en.wikipedia.org/wiki/Jordan_normal_form

    By the rank-nullity theorem, dim(ker(A−λI))=n-r, so t=n-r-s, and so the number of vectors in the potential basis is equal to n. To show linear independence, suppose some linear combination of the vectors is 0.

  4. Category:Theorems in linear algebra - Wikipedia

    en.wikipedia.org/wiki/Category:Theorems_in...

    Download as PDF; Printable version; In other projects Wikidata item; Appearance. move to sidebar hide. ... Ranknullity theorem; Rouché–Capelli theorem; S. Schur ...

  5. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    The dimension of the row space is called the rank of the matrix. This is the same as the maximum number of linearly independent rows that can be chosen from the matrix, or equivalently the number of pivots. For example, the 3 × 3 matrix in the example above has rank two. [9] The rank of a matrix is also equal to the dimension of the column space.

  6. List of theorems - Wikipedia

    en.wikipedia.org/wiki/List_of_theorems

    Principal axis theorem (linear algebra) Ranknullity theorem (linear algebra) Rouché–Capelli theorem (Linear algebra) Sinkhorn's theorem (matrix theory) Specht's theorem (matrix theory) Spectral theorem (linear algebra, functional analysis) Sylvester's determinant theorem (determinants) Sylvester's law of inertia (quadratic forms)

  7. Wikipedia:Reference desk/Archives/Mathematics/2017 March 27

    en.wikipedia.org/wiki/Wikipedia:Reference_desk/...

    Use the given information to find the rank of the linear transformation T where T : V → W. The null space of T : P 5 → P 5 is P 5. I used the ranknullity theorem and produced the following: rank(T) + nullity(T) = dim(V) nullity(T) = 6, dim(V) = 6 rank(T) + 6 = 6 rank(T) = 0. Is this result correct? I feel like I erred somewhere.

  8. Matrix (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Matrix_(mathematics)

    For example, a 2,1 represents the element at the second row and first column of the matrix. In mathematics, a matrix (pl.: matrices) is a rectangular array or table of numbers, symbols, or expressions, with elements or entries arranged in rows and columns, which is used to represent a mathematical object or property of such an object.

  9. Category:Isomorphism theorems - Wikipedia

    en.wikipedia.org/wiki/Category:Isomorphism_theorems

    These theorems are generalizations of some of the fundamental ideas from linear algebra, notably the ranknullity theorem, and are encountered frequently in group theory. The isomorphism theorems are also fundamental in the field of K-theory , and arise in ostensibly non-algebraic situations such as functional analysis (in particular the ...